Integrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). This paper conducts a comprehensive literature review of ML-based RT-FDD. Out of 805 documents, 29 studies were identified as noteworthy for presenting innovative methods that address the complexities and challenges associated with fault detection. While ML-based RT-FDD offers different benefits, including fault prediction accuracy, it faces challenges in data quality, model interpretability, and integration complexities. This review identifies a gap in industrial implementation outcomes that opens new research opportunities. Future Fault Detection and Diagnosis (FDD) research may prioritize standardized datasets to ensure reproducibility and facilitate comparative evaluations. Furthermore, there is a pressing need to refine techniques for handling unbalanced datasets and improving feature extraction for temporal series data. Implementing Explainable Artificial Intelligence (AI) (XAI) tailored to industrial fault detection is imperative for enhancing interpretability and trustworthiness. Subsequent studies must emphasize comprehensive comparative evaluations, reducing reliance on specialized expertise, documenting real-world outcomes, addressing data challenges, and bolstering real-time capabilities and integration. By addressing these avenues, the field can propel the advancement of ML-based RT-FDD methodologies, ensuring their effectiveness and relevance in industrial contexts.

The localized surface plasmon resonance (LSPR) due to light–particle interaction and its dependence on the surrounding medium have been widely manipulated for sensing applications. The sensing efficiency is governed by the refractive index-based sensitivity (${\eta }_{RIS}$) and the full width half maximum (FWHM) of the LSPR spectra. Thereby, a sensor with high precision must possess both requisites: an effective ${\eta }_{RIS}$ and a narrow FWHM of plasmon spectrum. Moreover, complex nanostructures are used for molecular sensing applications due to their good ${\eta }_{RIS}$ values but without considering the wide-band nature of the LSPR spectrum, which decreases the detection limit of the plasmonic sensor. In this article, a novel, facile and label-free solution-based LSPR immunosensor was elaborated based upon LSPR features such as extinction spectrum and localized field enhancement. We used a 3D full-wave field analysis to evaluate the optical properties and to optimize the appropriate size of spherical-shaped gold nanoparticles (Au NPs). We found a change in Au NPs’ radius from 5 nm to 50 nm, and an increase in spectral resonance peak depicted as a red-shift from 520 nm to 552 nm. Using this fact, important parameters that can be attributed to the LSPR sensor performance, namely the molecular sensitivity, FWHM, ${\eta }_{RIS}$, and figure of merit (FoM), were evaluated. Moreover, computational simulations were used to assess the optimized size (radius = 30 nm) of Au NPs with high FoM (2.3) and sharp FWHM (44 nm). On the evaluation of the platform as a label-free molecular sensor, Campbell’s model was performed, indicating an effective peak shift in the adsorption of the dielectric layer around the Au NP surface. For practical realization, we present an LSPR sensor platform for the identification of dengue NS1 antigens. The results present the system’s ability to identify dengue NS1 antigen concentrations with the limit of quantification measured to be 0.07 μg/mL (1.50 nM), evidence that the optimization approach used for the solution-based LSPR sensor provides a new paradigm for engineering immunosensor platforms.

We report a new variation of the conventional Z-scan method to characterize the third-order optical nonlinearity of photonic materials. By exploiting the combination of the eclipse Z-scan with a thermal nonlinearity management technique, we demonstrate an improvement in sensitivity and flexibility of the method to simultaneously characterize the thermal and nonthermal nonlinearity of optical materials. The method is demonstrated by measuring the nonlinear refractive index in CS₂, SiO₂ and H₂O, standard materials, and also in a biomaterial, the amino acid Tryptophan in water solution, using a femtosecond Ti-Sapphire laser operating at 76MHz repetition rate.

Third-order nonlinearity one order of magnitude larger than silica is measured in bismuth-borate glasses presenting a fast response $(<200\mathrm{fs})$ . The results for the sign and magnitude of the nonlinearity were obtained using a combination of the eclipse $Z$ scan with thermal nonlinearity managed $Z$ scan, whereas the Kerr shutter technique was employed to obtain the electronic time response of the nonlinearity, all performed with $76\mathrm{MHz}$ repetition rate $150\mathrm{fs}$ pulses at $800\mathrm{nm}$ . Conventional $Z$ scans in the picosecond regime at 532 and $1064\mathrm{nm}$ were also independently performed, yielding the values of the third-order nonlinear susceptibilities at those wavelengths. The results obtained for the femtosecond response, enhanced third-order nonlinearity of this glass (with respect to silica), place this glass system as an important tool in the development of photonics devices. Electro-optical modulators, optical switches, and frequency converters are some of the applications using second-order nonlinear properties of the Bi-glass based on the rectification model.

In this work we determine the third, fifth- and seventh-order nonresonant nonlinear optical properties of silver nanoparticles (9nm average diameter) colloids in aqueous solution under high intensity excitation. The nonlinear optical response and its dependence with the nanoparticles filling factor was measured and theoretically described. We show that for low inclusion concentration, the third order nonlinearity of the colloid can be described by the generalized Maxwell-Garnett model. With the increase of the nanoparticle concentration, changes in the medium nonlinearities was observed leading to high order effects. The fifth- and seventh- order susceptibilities were obtained for highly concentrated silver nanoparticle colloid and the data was supported by a theoretical model. The conventional Z-scan technique was employed, using 80 f s laser pulses at 800nm, in a regime of high pulse energy (µJ) and low repetition rate (1kHz).